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The main difference between RNN and CNN come from their structure of the Neural Network. Due to their specific design, CNNs are more fit for spatial data such as images whereas RNNs are more for temporal data that comes in sequence.
CNNs employ filters within convolutional layers to transform data. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series.
The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information in memory for the long period of time as compare to RNN. But the question is what is different in LSTM than RNN by which LSTMs are capable of maintaining long term temporal dependencies (remembering information for long period of time).
Writing an email is something we do while walking on the road also. The most official mode of communication, So, have you ever seen the ‘Smart Compose’ feature automatically working in your Gmail giving you instant ideas to finish sentences while composing an email? This is one of the different use-instances of language models utilized in Natural Language Processing (NLP). A language model is the core heart of the present day Natural Language Processing (NLP) domain. It’s a measurable device that investigates the example of human language for the forecast of words.
ROC AUC curve helps you to determine the threshold of binary classification problems in machine learning. In Machine Learning classification problems are based on the probability value and its not always correct to have the threshold as 0.5. It depends on the type and domain of the problem. For example in a legal case you don’t want the false positive to be high or it should be at least as possible. so the threshold in this case would be very high. the term AUC that is Area under curve tells us the model goodness of fit. It is used to do the comparative analysis between different classifiers and identify which one is performing good.
Data Analyst job is to explore the data, interpret it and finally convert it to information. This information will then help decision makers to take business related decisions. As a Data Analyst your roles and responsibilities includes starting from data collection, interpretation, visualization, dashboard building and finally story telling around the information derived from data.
Let me start with simple question. Can we compare Mango and Apple? Both have different features in terms of tastes, sweetness, health benefits etc. So comparison can be performed between similar entities else it will be biased. Same logic applies to Machine Learning as well. Feature Scaling in Machine Learning brings features to the same scale before we apply any comparison or model building. Normalization and Standardization are the two frequently used techniques of Feature Scaling in Machine Learning.
